Analysis of India’s BC Data
Introduction
Moniroting Station
Location Details
| Location | AQ_Type | Lat | Long |
|---|---|---|---|
| Vill_1 | RAMP | 26.5318 | 80.38050 |
| Vill_2 | RAMP | 26.5251 | 80.41490 |
| KNP | CPCB | 26.4499 | 80.33190 |
| LKO_Sch | CPCB | 26.7785 | 80.93130 |
| LKO_Ind | CPCB | 26.8332 | 80.89660 |
| Hamirpur | IGPCARE | 25.8154 | 79.91875 |
Stations in Map
Household and Village Details
We considered one household from each village. These two households were set up for indoor monitoring in addition to outdoor monitoring in the village center.
Building Structure
Household Structure
Survey Results
| Parameter | Village1_HH | Village2_HH |
|---|---|---|
| Population | A2C0 | A5C2 |
| Prim CookFuel | Dung Cake | Dunc Cake |
| Sec CookFuel | Kerosene/Firewood | Diesel |
| Kitchen Type | Indoor | Outdoor Enclosed Kitchen |
| LPG Conn | Yes | Yes |
| No. Meals | 2 | 2 |
AQ Data
Several data sets were utilized in this study.
- Rural Monitoring
- Temporal Frequency: 5 min
- Bhawani Kheda (Village 1) and Naikani Kheda (Village 2)
- Household Indoor Air Quality
- Village Outdoor Air Quality
- Pollutants: (Raw data considered)
- RAMP: PM2.5, CO, Temperature (T), Relative Humidity (RH)
- MA300: BC, BCbb (biomass burning component), BCff (fossil fuel component), AAE (Angstrom Exponent), pctBB (BCbb percentage of total BC)
- Regulatory Monitoring
- LKO_Sch: CPCB monitoring station in Lucknow Central School
- Pollutants: PM2.5, CO, Temperature (AT), Relative Humidity (RH), NO, NOx, Ozone
- Temporal Frequency: 15 min
- Distance from Rural Monitoring: 59 km
- LKO_Ind: CPCB monitoring station in Talkatora Industrial Belt
- Pollutants: PM2.5, CO, Temperature (AT), Relative Humidity (RH), NO, NOx, NO2, SO2
- Temporal Frequency: 15 min
- Distance from Rural Monitoring: 59 km
- KNP: CPCB monitoring station in Kanpur Neheru Nagar
- Pollutants: PM2.5, Temperature (AT), Relative Humidity (RH), NO, NOx, Ozone
- Temporal Frequency: 1 hour
- Distance from Rural Monitoring: 12 km
- IGPCARE: Long term monitoring station in a rural site in a nearby
district
- Data not available publicly
- Pollutants: BC, BrC (Brown Carbon), O3, PM2.5
- Rural regulatory monitoring site
- operated by University of Gothenburg (Dr. Ravi Kant Pathak)
- Distance from Present Rural Monitoring: 93 km
- Note: Useful for comparing trends in rural BC pollution (MA300 vs AE)
- Related Publication: https://pubs.rsc.org/en/content/articlehtml/2022/ea/d1ea00083g
| House | n | positive | negative |
|---|---|---|---|
| Village1_Outdoor | 2054 | 100 | 0 |
| Village1_Indoor | 1942 | 100 | 0 |
| Village2_Outdoor | 3815 | 100 | 0 |
| Village2_Indoor | 3223 | 100 | 0 |
Summary Statisics
Here I am focusing on the combustion generated pollutants, i.e. PM2.5, BC and CO. The mean concentration and coefficient of variation (standard deviation/mean) have been calculated and presented for indoor and outdoor in village 1 and 2.| House | meanPM2.5 | covPM2.5 | meanBC | covBC | meanCO | covCO |
|---|---|---|---|---|---|---|
| Village1_Outdoor | 61.22 | 0.50 | 16.19 | 0.54 | 821.30 | 0.44 |
| Village1_Indoor | 64.19 | 0.44 | 16.26 | 0.42 | 1229.27 | 1.24 |
| Village2_Outdoor | 58.75 | 0.53 | 14.57 | 0.53 | 953.00 | 0.38 |
| Village2_Indoor | 63.43 | 0.71 | 14.12 | 0.51 | 1690.40 | 1.10 |
Note:
- Units: PM2.5 and BC are in \(\mu g/m^3\); CO in ppb
- Fill Here
Visualize Diurnal Change in Pollutants
Diurnal PM2.5
Diurnal CO
Diurnal BC
Reference measurement vs RAMP data
Analysis of BC concentration
BC concentration by activity period
HH_3 %>% ggplot(.,aes(x = activity, y = BC))+
geom_boxplot()+
theme_pubr()
### BC concentration by Location~Activity
HH_3 %>% ggplot(.,aes(x = activity, y = BC, fill = House))+
geom_boxplot(position=position_dodge(0.8))+
theme_pubr()
### BC concentration by DayPart~Activity
HH_3 %>% ggplot(.,aes(x = DayPart, y = BC, fill = House))+
geom_boxplot(position=position_dodge(0.8))+
theme_pubr()
## Source Apportionment Results
BCbb by Location~Activity
HH_3 %>% ggplot(.,aes(x = activity, y = BCbb, fill = House))+
geom_boxplot(position=position_dodge(0.8))+
theme_pubr()
### BCff by DayPart~Activity
HH_3 %>% ggplot(.,aes(x = DayPart, y = BCff, fill = House))+
geom_boxplot(position=position_dodge(0.8))+
theme_pubr()Spatial Data
Map of Population Density
r = raster::raster("02_data/AQ3_Popul_IGP.nc")
pal <- colorBin(c("#8D5524", "#FFDBAC"), raster::values(r),
bins = 5, na.color = "transparent")
raster::crs(r) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
leaflet() %>% addTiles() %>% addProviderTiles(providers$CartoDB.Positron) %>%
addRasterImage(r, colors = pal, opacity = 0.8) %>%
addLegend(pal = pal, values = raster::values(r),
title = "Popul Den") %>%
addPolygons(data = in_d, weight = 1, fill = FALSE, color = "#120f01") %>%
fitBounds(76, 30, 89, 21)## Warning in colors(.): Some values were outside the color scale and will be
## treated as NA